If we want to broaden participation, we must educate our students based on the early 17th-century origins of the word "computer," a human who performs calculations.1 Computers were exclusively human until the early 19th century when English polymath and inventor Charles Babbage introduced the Difference Engine, the first mechanical computer. The term "human computer" was then used to differentiate a person who computes from a mechanical computer. Human computers were often women who undertook long and tedious calculations to power some of the most significant advances in science, industry, and space technology in the 20th century.

Under the leadership of the Fraunhofer Institutes for Intelligent Analysis and Information Systems (IAIS) and for Integrated Circuits (IIS), the OpenGPT-X project is starting with the goal of developing a large AI language model for Europe. Particular attention is being paid to data protection as well as European language diversity. "International competitors have already recognized the enormous disruptive potential of AI language technologies for business, industry and society. A European AI language model like OpenGPT-X is therefore imperative to ensure Europe's digital sovereignty and market independence," says Dr. Nicolas Flores-Herr, head of the project at Fraunhofer IAIS. Due to the high technical requirements, such as computing power, such powerful language models can so far only be implemented by large companies or consortia.

Artificial intelligence or AI applies to a machine that can mimic actions like a human by analyzing the data. Machines are programmed to learn from data so that they can solve complex problems. Every sector and industry has benefited from AI. Another area where AI is evolving to be a handy tool is video editing. AI in video editing is at an early stage.

The Arm Compute Library is a collection of low-level machine learning functions optimized for Cortex-A CPU and Mali GPU architectures.

… Speech’ Site Will Use Big-Tech Artificial Intelligence to Censor Posts … Valley artificial intelligence (AI) to censor posts on its network.

The machine rapidly gives an indication of how a flavour can be created and how much of an ingredient should be included. And Sam can hold the line between Mr Salord's team of flavourists and public tastes, refereeing decisions when the flavourists' view differs from that of a consumer panel.

The most common way to listen to recorded music nowadays is via streaming platforms which provide access to tens of millions of tracks. To assist users in effectively browsing these large catalogs, the integration of Music Recommender Systems (MRSs) has become essential. Current real-world MRSs are often quite complex and optimized for recommendation accuracy. They combine several building blocks based on collaborative filtering and content-based recommendation. This complexity can hinder the ability to explain recommendations to end users, which is particularly important for recommendations perceived as unexpected or inappropriate. While pure recommendation performance often correlates with user satisfaction, explainability has a positive impact on other factors such as trust and forgiveness, which are ultimately essential to maintain user loyalty. In this article, we discuss how explainability can be addressed in the context of MRSs. We provide perspectives on how explainability could improve music recommendation algorithms and enhance user experience. First, we review common dimensions and goals of recommenders' explainability and in general of eXplainable Artificial Intelligence (XAI), and elaborate on the extent to which these apply -- or need to be adapted -- to the specific characteristics of music consumption and recommendation. Then, we show how explainability components can be integrated within a MRS and in what form explanations can be provided. Since the evaluation of explanation quality is decoupled from pure accuracy-based evaluation criteria, we also discuss requirements and strategies for evaluating explanations of music recommendations. Finally, we describe the current challenges for introducing explainability within a large-scale industrial music recommender system and provide research perspectives.

Have you heard about fake news, AI, and deep fake? This blog post is going to introduce you to all three of these terms, giving a broad overview of just what is going on. Are we living in a science fiction film? Are computers going to bring about the end of civilization as we know it? This isn't fiction, so I'm not talking about Brave New World (although you should read that book).

To many, Artificial Intelligence (AI), is still in the realm of science fiction: decades away, and far removed from our current reality. However, increasingly, we are interfacing with platforms and systems powered by AI, such as smart watches and virtual assistants, such as Siri and Alexa. More importantly, in the coming decade, AI will drive and underpin a broad range of processes, and consequently change the ways we live and work. This episode is also available on SoundCloud, Apple iTunes, Google Play Music, Spotify, Amazon Music (NEW!) and on Stitcher! Over the past few months, there have been some new developments in the AI space that highlight the fact that AI is developing rapidly.

Those looking to add to their Google home setup can get the biggest Nest smart display for less today. Adorama has a one-day-only sale that knocks $60 off the Nest Hub Max, bringing it down to $169. That's even cheaper than we saw it a couple of weeks ago when a bunch of Nest gadgets were discounted across the web, and it's $11 less than the device's Black Friday price last year. The Nest Hub Max earned a score of 86 from us when it first came out in 2019 and it remains a good option for those that rely on the Google Assistant and want a larger home hub with advanced smart features. It has a spacious 10-inch HD touchscreen on which you can do things like take a Zoom call, watch YouTube or Netflix and control all of the smart lights, thermostats and other gadgets in your home.